Factor Analysis in R

I'm trying to get a better understanding on FA, hope you can take a look at this, my biggest problem is how to interpret FA model in R.

My results look like this: What values in my results should I be looking at and what is a good indication of FA analysis?

Call:
factanal(x = m2, factors = 2)

Un开发者_如何学Pythoniquenesses:
v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12
0.005 0.324 0.344 0.092 0.084 0.128 0.271 0.272 0.398 0.384 0.540 0.472

Loadings:
Factor1 Factor2
v1 0.847 0.527
v2 0.818
v3 0.733 0.344
v4 0.938 0.169
v5 0.949 0.125
v6 0.825 0.437
v7 0.701 0.488
v8 0.646 0.557
v9 0.467 0.619
v10 0.665 0.417
v11 0.525 0.429
v12 0.581 0.436

Factor1 Factor2
SS loadings 5.905 2.780
Proportion Var 0.492 0.232
Cumulative Var 0.492 0.724

Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 410.82 on 43 degrees of freedom.
The p-value is 1.59e-61

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I posted an example factor analysis in R looking at the factor structure of a personality test. It shows how to extract some of the common information that you might want (e.g., communalities; tests of number of factors; variance explained by factors; rotations; etc.).

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In general, with FA you cannot directly interpret the factor loadings because they are not unique (rotation problem). Other than that, I hate to sound like psychologist (statistician joke...), but you have a low p-value!

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Because here there is not a reproducible example but just an output. I will give the suggestion for the next step of EFA for you. Here I think you need verify the reliability of your model. Generally, alpha() and splitHalf() functions are recommended, which are in the psych package. If you find your model's reliabilities are both larger than 0.8, fortunately you may get a good model.

There is an minimal examples on the DataCamp for you to go deeper.